sparse-array | 易学教程

extract sparse rows from sparse matrix in r

阅读更多关于 extract sparse rows from sparse matrix in r

问题 I have a large sparse matrix to analyze in R. For instance: i <- c(1,3:8); j <- c(2,9,6:10); x <- 7 * (1:7) (A <- sparseMatrix(i, j, x = x)) [1,] . 7 . . . . . . . . [2,] . . . . . . . . . . [3,] . . . . . . . . 14 . [4,] . . . . . 21 . . . . [5,] . . . . . . 28 . . . [6,] . . . . . . . 35 . . [7,] . . . . . . . . 42 . [8,] . . . . . . . . . 49 I want to extract the i -th row from this matrix, as a sparse vector. If I write (x=A[1,]) I obtain [1] 0 7 0 0 0 0 0 0 0 0 but what I would like is

Matlab: First Non-zero element of each row or column

阅读更多关于 Matlab: First Non-zero element of each row or column

问题 For example, A = [ -1 0 -2 0 0 2 8 0 1 0 0 0 3 0 -2 0 -3 2 0 0 1 2 0 0 -4]; how can I get a vector of the first nonzero elements of each row? 回答1: You can use max : >> [sel, c] = max( A ~=0, [], 2 ); Rows for which sel equalse zero - are all zeros and the corresponding column in c should be ignored. Result: >> [sel c]= max( A~=0, [], 2 ) sel = 1 1 1 1 1 c = 1 1 3 2 1 In order to find the first non-zero row index (for each column) you just need to apply max on the first dimension: >> [sel r] =

Sparse array support in HDF5

阅读更多关于 Sparse array support in HDF5

问题 I need to store a 512^3 array on disk in some way and I'm currently using HDF5. Since the array is sparse a lot of disk space gets wasted. Does HDF5 provide any support for sparse array ? 回答1: Chunked datasets (H5D_CHUNKED) allow sparse storage but depending on your data, the overhead may be important. Take a typical array and try both sparse and non-sparse and then compare the file sizes, then you will see if it is really worth. 回答2: One workaround is to create the dataset with a compression

Which is the best way to implement a sparse vector in Java?

阅读更多关于 Which is the best way to implement a sparse vector in Java?

问题 Which is the best way to implement a sparse vector in Java? Of course the good thing would be to have something that can be manipulated quite easily (normalization, scalar product and so on) Thanks in advance 回答1: MTJ has a Sparse Vector class. It has norm functions (1-norm 2-norm and ∞-norm) and dot product functions. 回答2: JScience has a SparseVector implementation that is part of its linear algebra package. 回答3: You can also try to look at la4j's CompressedVector implementation. It uses

Is Cocoa's NSMutableArray sparse?

阅读更多关于 Is Cocoa's NSMutableArray sparse?

问题 If I create an NSMutableArray that might have up to 2^16 elements, but will mostly be empty, will I be wasting space or is NSMutableArray implemented as a sparse array? 回答1: Elements in an NSArray can't be empty and there's no "default" value. To represent nil , you'd usually use the singleton [NSNull null] , which is still a reference to an object so it consumes memory (the pointer). I'd consider using NSDictionary (or NSMutableDictionary ) with numeric ( NSNumber ) keys instead. 回答2: No

How to store sparsearray in bundle

阅读更多关于 How to store sparsearray in bundle

问题 I have a SparseArray<myObject> and want to store it in bundle in onSaveInstanceState method in my activity and restore it in oncreate . I found putSparseParcelableArray method for put SparseArray in bundle and did this in onSaveInstanceState method: bundle.putSparseParcelableArray("mySparseArray", mySparseArray); But eclips shows this error: The method putSparseParcelableArray(String, SparseArray<? extends Parcelable>) in the type Bundle is not applicable for the arguments (String,

Sparse O(1) array with indices being consecutive products

阅读更多关于 Sparse O(1) array with indices being consecutive products

I'd like to pre-calculate an array of values of some unary function f . I know that I'll only need the values for f(x) where x is of the form of a*b , where both a and b are integers in range 0..N . The obvious time-optimized choice is just to make an array of size N*N and just pre-calculate just the elements which I'm going to read later. For f(a*b) , I'd just check and set tab[a*b] . This is the fastest method possible - however, this is going to take a lot of space as there are lots of indices in this array (starting with N+1 ) which will never by touched. Another solution is to make a

How to wrap Eigen::SparseMatrix over preexistant 3-standard compress row/colum arrays

阅读更多关于 How to wrap Eigen::SparseMatrix over preexistant 3-standard compress row/colum arrays

NOTE: I allready asked this question, but it was closed because of "too broad" without much explanation. I can't see how this question could be more specific (it deals with a specific class of a specific library for a specific usage...), so I assume that it was something like a "moderator's mistake" and ask it again... I would like to perfom sparse matrix/matrix multiplication using Eigen on sparse matrices. These matrices are already defined in the code I am working on in standard 3-arrays compressed row/column strorage. Then I would like to use the Eigen::SparseMatrix class as a wrapper on

How to quickly compact a sparse array with CUDA C?

阅读更多关于 How to quickly compact a sparse array with CUDA C?

问题 Summary Array [A - B - - - C] in device memory but want [A B C] - what's the quickest way with CUDA C? Context I have an array A of integers on device (GPU) memory. At each iteration, I randomly choose a few elements that are larger than 0 and subtract 1 from them. I maintain a sorted lookup array L of those elements that are equal to 0: Array A: @ iteration i: [0 1 0 3 3 2 0 1 2 3] @ iteration i + 1: [0 0 0 3 2 2 0 1 2 3] Lookup for 0-elements L: @ iteration i: [0 - 2 - - - 6 - - -] -> want

how to implement a sparse_vector class

阅读更多关于 how to implement a sparse_vector class

I am implementing a templated sparse_vector class. It's like a vector, but it only stores elements that are different from their default constructed value. So, sparse_vector would store the lazily-sorted index-value pairs for all indices whose value is not T(). I am basing my implementation on existing sparse vectors in numeric libraries-- though mine will handle non-numeric types T as well. I looked at boost::numeric::ublas::coordinate_vector and eigen::SparseVector . Both store: size_t* indices_; // a dynamic array T* values_; // a dynamic array int size_; int capacity_; Why don't they